using System;

namespace TorchSharp
{
    public static partial class torch
    {
        public static partial class utils
        {
            public static partial class tensorboard
            {
                internal static partial class GifEncoder
                {
                    /// <summary>
                    /// NeuQuant Neural-Net Quantization Algorithm
                    /// ------------------------------------------
                    ///
                    /// Copyright (c) 1994 Anthony Dekker
                    ///
                    /// NEUQUANT Neural-Net quantization algorithm by Anthony Dekker, 1994. See
                    /// "Kohonen neural networks for optimal colour quantization" in "Network:
                    /// Computation in Neural Systems" Vol. 5 (1994) pp 351-367. for a discussion of
                    /// the algorithm.
                    ///
                    /// Any party obtaining a copy of these files from the author, directly or
                    /// indirectly, is granted, free of charge, a full and unrestricted irrevocable,
                    /// world-wide, paid up, royalty-free, nonexclusive right and license to deal in
                    /// this software and documentation files (the "Software"), including without
                    /// limitation the rights to use, copy, modify, merge, publish, distribute,
                    /// sublicense, and/or sell copies of the Software, and to permit persons who
                    /// receive copies from any such party to do so, with the only requirement being
                    /// that this copyright notice remain intact.
                    ///
                    /// https://cs.android.com/android/platform/superproject/+/master:external/glide/third_party/gif_encoder/src/main/java/com/bumptech/glide/gifencoder/NeuQuant.java
                    /// </summary>
                    internal class NeuQuant
                    {
                        protected static readonly int netsize = 256; /* number of colours used */
                        /* four primes near 500 - assume no image has a length so large */
                        /* that it is divisible by all four primes */
                        protected static readonly int prime1 = 499;
                        protected static readonly int prime2 = 491;
                        protected static readonly int prime3 = 487;
                        protected static readonly int prime4 = 503;
                        protected static readonly int minpicturebytes = (3 * prime4);
                        /* minimum size for input image */
                        /* Program Skeleton
                           ----------------
                           [select samplefac in range 1..30]
                           [read image from input file]
                           pic = (unsigned char*) malloc(3*width*height);
                           initnet(pic,3*width*height,samplefac);
                           learn();
                           unbiasnet();
                           [write output image header, using writecolourmap(f)]
                           inxbuild();
                           write output image using inxsearch(b,g,r)      */

                        /* Network Definitions
                           ------------------- */
                        protected static readonly int maxnetpos = (netsize - 1);
                        protected static readonly int netbiasshift = 4; /* bias for colour values */
                        protected static readonly int ncycles = 100; /* no. of learning cycles */

                        /* defs for freq and bias */
                        protected static readonly int intbiasshift = 16; /* bias for fractions */
                        protected static readonly int intbias = (((int)1) << intbiasshift);
                        protected static readonly int gammashift = 10; /* gamma = 1024 */
                        protected static readonly int gamma = (((int)1) << gammashift);
                        protected static readonly int betashift = 10;
                        protected static readonly int beta = (intbias >> betashift); /* beta = 1/1024 */
                        protected static readonly int betagamma =
                            (intbias << (gammashift - betashift));

                        /* defs for decreasing radius factor */
                        protected static readonly int initrad = (netsize >> 3); /* for 256 cols, radius starts */
                        protected static readonly int radiusbiasshift = 6; /* at 32.0 biased by 6 bits */
                        protected static readonly int radiusbias = (((int)1) << radiusbiasshift);
                        protected static readonly int initradius = (initrad * radiusbias); /* and decreases by a */
                        protected static readonly int radiusdec = 30; /* factor of 1/30 each cycle */

                        /* defs for decreasing alpha factor */
                        protected static readonly int alphabiasshift = 10; /* alpha starts at 1.0 */
                        protected static readonly int initalpha = (((int)1) << alphabiasshift);

                        protected int alphadec; /* biased by 10 bits */

                        /* radbias and alpharadbias used for radpower calculation */
                        protected static readonly int radbiasshift = 8;
                        protected static readonly int radbias = (((int)1) << radbiasshift);
                        protected static readonly int alpharadbshift = (alphabiasshift + radbiasshift);
                        protected static readonly int alpharadbias = (((int)1) << alpharadbshift);

                        /* Types and Global Variables
                        -------------------------- */

                        protected byte[] thepicture; /* the input image itself */
                        protected int lengthcount; /* lengthcount = H*W*3 */

                        protected int samplefac; /* sampling factor 1..30 */

                        //   typedef int pixel[4];                /* BGRc */
                        protected int[][] network; /* the network itself - [netsize][4] */

                        protected int[] netindex = new int[256];
                        /* for network lookup - really 256 */

                        protected int[] bias = new int[netsize];
                        /* bias and freq arrays for learning */
                        protected int[] freq = new int[netsize];
                        protected int[] radpower = new int[initrad];
                        /* radpower for precomputation */

                        /// <summary>
                        /// Initialise network in range (0,0,0) to (255,255,255) and set parameters
                        /// </summary>
                        /// <param name="thepic"></param>
                        /// <param name="len"></param>
                        /// <param name="sample"></param>
                        public NeuQuant(byte[] thepic, int len, int sample)
                        {

                            int i;
                            int[] p;

                            thepicture = thepic;
                            lengthcount = len;
                            samplefac = sample;

                            network = new int[netsize][];
                            for (i = 0; i < netsize; i++) {
                                network[i] = new int[4];
                                p = network[i];
                                p[0] = p[1] = p[2] = (i << (netbiasshift + 8)) / netsize;
                                freq[i] = intbias / netsize; /* 1/netsize */
                                bias[i] = 0;
                            }
                        }

                        public byte[] ColorMap()
                        {
                            byte[] map = new byte[3 * netsize];
                            int[] index = new int[netsize];
                            for (int i = 0; i < netsize; i++)
                                index[network[i][3]] = i;
                            int k = 0;
                            for (int i = 0; i < netsize; i++) {
                                int j = index[i];
                                map[k++] = (byte)(network[j][0]);
                                map[k++] = (byte)(network[j][1]);
                                map[k++] = (byte)(network[j][2]);
                            }
                            return map;
                        }

                        /// <summary>
                        /// Insertion sort of network and building of netindex[0..255] (to do after unbias)
                        /// </summary>
                        public void Inxbuild()
                        {

                            int i, j, smallpos, smallval;
                            int[] p;
                            int[] q;
                            int previouscol, startpos;

                            previouscol = 0;
                            startpos = 0;
                            for (i = 0; i < netsize; i++) {
                                p = network[i];
                                smallpos = i;
                                smallval = p[1]; /* index on g */
                                /* find smallest in i..netsize-1 */
                                for (j = i + 1; j < netsize; j++) {
                                    q = network[j];
                                    if (q[1] < smallval) { /* index on g */
                                        smallpos = j;
                                        smallval = q[1]; /* index on g */
                                    }
                                }
                                q = network[smallpos];
                                /* swap p (i) and q (smallpos) entries */
                                if (i != smallpos) {
                                    j = q[0];
                                    q[0] = p[0];
                                    p[0] = j;
                                    j = q[1];
                                    q[1] = p[1];
                                    p[1] = j;
                                    j = q[2];
                                    q[2] = p[2];
                                    p[2] = j;
                                    j = q[3];
                                    q[3] = p[3];
                                    p[3] = j;
                                }
                                /* smallval entry is now in position i */
                                if (smallval != previouscol) {
                                    netindex[previouscol] = (startpos + i) >> 1;
                                    for (j = previouscol + 1; j < smallval; j++)
                                        netindex[j] = i;
                                    previouscol = smallval;
                                    startpos = i;
                                }
                            }
                            netindex[previouscol] = (startpos + maxnetpos) >> 1;
                            for (j = previouscol + 1; j < 256; j++)
                                netindex[j] = maxnetpos; /* really 256 */
                        }

                        /// <summary>
                        /// Main Learning Loop
                        /// </summary>
                        public void Learn()
                        {

                            int i, j, b, g, r;
                            int radius, rad, alpha, step, delta, samplepixels;
                            byte[] p;
                            int pix, lim;

                            if (lengthcount < minpicturebytes)
                                samplefac = 1;
                            alphadec = 30 + ((samplefac - 1) / 3);
                            p = thepicture;
                            pix = 0;
                            lim = lengthcount;
                            samplepixels = lengthcount / (3 * samplefac);
                            delta = samplepixels / ncycles;
                            alpha = initalpha;
                            radius = initradius;

                            rad = radius >> radiusbiasshift;
                            if (rad <= 1)
                                rad = 0;
                            for (i = 0; i < rad; i++)
                                radpower[i] =
                                    alpha * (((rad * rad - i * i) * radbias) / (rad * rad));

                            //fprintf(stderr,"beginning 1D learning: initial radius=%d\n", rad);

                            if (lengthcount < minpicturebytes)
                                step = 3;
                            else if ((lengthcount % prime1) != 0)
                                step = 3 * prime1;
                            else {
                                if ((lengthcount % prime2) != 0)
                                    step = 3 * prime2;
                                else {
                                    if ((lengthcount % prime3) != 0)
                                        step = 3 * prime3;
                                    else
                                        step = 3 * prime4;
                                }
                            }

                            i = 0;
                            while (i < samplepixels) {
                                b = (p[pix + 0] & 0xff) << netbiasshift;
                                g = (p[pix + 1] & 0xff) << netbiasshift;
                                r = (p[pix + 2] & 0xff) << netbiasshift;
                                j = Contest(b, g, r);

                                Altersingle(alpha, j, b, g, r);
                                if (rad != 0)
                                    Alterneigh(rad, j, b, g, r); /* alter neighbours */

                                pix += step;
                                if (pix >= lim)
                                    pix -= lengthcount;

                                i++;
                                if (delta == 0)
                                    delta = 1;
                                if (i % delta == 0) {
                                    alpha -= alpha / alphadec;
                                    radius -= radius / radiusdec;
                                    rad = radius >> radiusbiasshift;
                                    if (rad <= 1)
                                        rad = 0;
                                    for (j = 0; j < rad; j++)
                                        radpower[j] =
                                            alpha * (((rad * rad - j * j) * radbias) / (rad * rad));
                                }
                            }
                            //fprintf(stderr,"finished 1D learning: readonly alpha=%f !\n",((float)alpha)/initalpha);
                        }

                        /// <summary>
                        /// Search for BGR values 0..255 (after net is unbiased) and return colour index
                        /// </summary>
                        /// <param name="b"></param>
                        /// <param name="g"></param>
                        /// <param name="r"></param>
                        /// <returns></returns>
                        public int Map(int b, int g, int r)
                        {

                            int i, j, dist, a, bestd;
                            int[] p;
                            int best;

                            bestd = 1000; /* biggest possible dist is 256*3 */
                            best = -1;
                            i = netindex[g]; /* index on g */
                            j = i - 1; /* start at netindex[g] and work outwards */

                            while ((i < netsize) || (j >= 0)) {
                                if (i < netsize) {
                                    p = network[i];
                                    dist = p[1] - g; /* inx key */
                                    if (dist >= bestd)
                                        i = netsize; /* stop iter */
                                    else {
                                        i++;
                                        if (dist < 0)
                                            dist = -dist;
                                        a = p[0] - b;
                                        if (a < 0)
                                            a = -a;
                                        dist += a;
                                        if (dist < bestd) {
                                            a = p[2] - r;
                                            if (a < 0)
                                                a = -a;
                                            dist += a;
                                            if (dist < bestd) {
                                                bestd = dist;
                                                best = p[3];
                                            }
                                        }
                                    }
                                }
                                if (j >= 0) {
                                    p = network[j];
                                    dist = g - p[1]; /* inx key - reverse dif */
                                    if (dist >= bestd)
                                        j = -1; /* stop iter */
                                    else {
                                        j--;
                                        if (dist < 0)
                                            dist = -dist;
                                        a = p[0] - b;
                                        if (a < 0)
                                            a = -a;
                                        dist += a;
                                        if (dist < bestd) {
                                            a = p[2] - r;
                                            if (a < 0)
                                                a = -a;
                                            dist += a;
                                            if (dist < bestd) {
                                                bestd = dist;
                                                best = p[3];
                                            }
                                        }
                                    }
                                }
                            }
                            return (best);
                        }
                        public byte[] Process()
                        {
                            Learn();
                            Unbiasnet();
                            Inxbuild();
                            return ColorMap();
                        }

                        /// <summary>
                        /// Unbias network to give byte values 0..255 and record position i to prepare for sort
                        /// </summary>
                        public void Unbiasnet()
                        {

                            int i;

                            for (i = 0; i < netsize; i++) {
                                network[i][0] >>= netbiasshift;
                                network[i][1] >>= netbiasshift;
                                network[i][2] >>= netbiasshift;
                                network[i][3] = i; /* record colour no */
                            }
                        }

                        /// <summary>
                        /// Move adjacent neurons by precomputed alpha*(1-((i-j)^2/[r]^2)) in radpower[|i-j|]
                        /// </summary>
                        /// <param name="rad"></param>
                        /// <param name="i"></param>
                        /// <param name="b"></param>
                        /// <param name="g"></param>
                        /// <param name="r"></param>
                        protected void Alterneigh(int rad, int i, int b, int g, int r)
                        {

                            int j, k, lo, hi, a, m;
                            int[] p;

                            lo = i - rad;
                            if (lo < -1)
                                lo = -1;
                            hi = i + rad;
                            if (hi > netsize)
                                hi = netsize;

                            j = i + 1;
                            k = i - 1;
                            m = 1;
                            while ((j < hi) || (k > lo)) {
                                a = radpower[m++];
                                if (j < hi) {
                                    p = network[j++];
                                    try {
                                        p[0] -= (a * (p[0] - b)) / alpharadbias;
                                        p[1] -= (a * (p[1] - g)) / alpharadbias;
                                        p[2] -= (a * (p[2] - r)) / alpharadbias;
                                    } catch (Exception) {
                                    } // prevents 1.3 miscompilation
                                }
                                if (k > lo) {
                                    p = network[k--];
                                    try {
                                        p[0] -= (a * (p[0] - b)) / alpharadbias;
                                        p[1] -= (a * (p[1] - g)) / alpharadbias;
                                        p[2] -= (a * (p[2] - r)) / alpharadbias;
                                    } catch (Exception) {
                                    }
                                }
                            }
                        }

                        /// <summary>
                        /// Move neuron i towards biased (b,g,r) by factor alpha
                        /// </summary>
                        /// <param name="alpha"></param>
                        /// <param name="i"></param>
                        /// <param name="b"></param>
                        /// <param name="g"></param>
                        /// <param name="r"></param>
                        protected void Altersingle(int alpha, int i, int b, int g, int r)
                        {

                            /* alter hit neuron */
                            int[] n = network[i];
                            n[0] -= (alpha * (n[0] - b)) / initalpha;
                            n[1] -= (alpha * (n[1] - g)) / initalpha;
                            n[2] -= (alpha * (n[2] - r)) / initalpha;
                        }

                        /// <summary>
                        /// Search for biased BGR values
                        /// </summary>
                        /// <param name="b"></param>
                        /// <param name="g"></param>
                        /// <param name="r"></param>
                        /// <returns></returns>
                        protected int Contest(int b, int g, int r)
                        {

                            /* finds closest neuron (min dist) and updates freq */
                            /* finds best neuron (min dist-bias) and returns position */
                            /* for frequently chosen neurons, freq[i] is high and bias[i] is negative */
                            /* bias[i] = gamma*((1/netsize)-freq[i]) */

                            int i, dist, a, biasdist, betafreq;
                            int bestpos, bestbiaspos, bestd, bestbiasd;
                            int[] n;

                            bestd = ~(((int)1) << 31);
                            bestbiasd = bestd;
                            bestpos = -1;
                            bestbiaspos = bestpos;

                            for (i = 0; i < netsize; i++) {
                                n = network[i];
                                dist = n[0] - b;
                                if (dist < 0)
                                    dist = -dist;
                                a = n[1] - g;
                                if (a < 0)
                                    a = -a;
                                dist += a;
                                a = n[2] - r;
                                if (a < 0)
                                    a = -a;
                                dist += a;
                                if (dist < bestd) {
                                    bestd = dist;
                                    bestpos = i;
                                }
                                biasdist = dist - ((bias[i]) >> (intbiasshift - netbiasshift));
                                if (biasdist < bestbiasd) {
                                    bestbiasd = biasdist;
                                    bestbiaspos = i;
                                }
                                betafreq = (freq[i] >> betashift);
                                freq[i] -= betafreq;
                                bias[i] += (betafreq << gammashift);
                            }
                            freq[bestpos] += beta;
                            bias[bestpos] -= betagamma;
                            return (bestbiaspos);
                        }
                    }
                }
            }
        }
    }
}
